Record:   Prev Next
作者 Lee, Jae K
書名 Statistical Bioinformatics : For Biomedical and Life Science Researchers
出版項 Hoboken : John Wiley & Sons, Incorporated, 2014
©2010
國際標準書號 9780470567630 (electronic bk.)
9780471692720
book jacket
版本 1st ed
說明 1 online resource (386 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
附註 Intro -- STATISTICAL BIOINFORMATICS -- CONTENTS -- PREFACE -- CONTRIBUTORS -- 1 ROAD TO STATISTICAL BIOINFORMATICS -- Challenge 1: Multiple-Comparisons Issue -- Challenge 2: High-Dimensional Biological Data -- Challenge 3: Small-n and Large-p Problem -- Challenge 4: Noisy High-Throughput Biological Data -- Challenge 5: Integration of Multiple, Heterogeneous Biological Data Information -- References -- 2 PROBABILITY CONCEPTS AND DISTRIBUTIONS FOR ANALYZING LARGE BIOLOGICAL DATA -- 2.1 Introduction -- 2.2 Basic Concepts -- 2.3 Conditional Probability and Independence -- 2.4 Random Variables -- 2.5 Expected Value and Variance -- 2.6 Distributions of Random Variables -- 2.7 Joint and Marginal Distribution -- 2.8 Multivariate Distribution -- 2.9 Sampling Distribution -- 2.10 Summary -- 3 QUALITY CONTROL OF HIGH-THROUGHPUT BIOLOGICAL DATA -- 3.1 Sources of Error in High-Throughput Biological Experiments -- 3.2 Statistical Techniques for Quality Control -- 3.3 Issues Specific to Microarray Gene Expression Experiments -- 3.4 Conclusion -- References -- 4 STATISTICAL TESTING AND SIGNIFICANCE FOR LARGE BIOLOGICAL DATA ANALYSIS -- 4.1 Introduction -- 4.2 Statistical Testing -- 4.3 Error Controlling -- 4.4 Real Data Analysis -- 4.5 Concluding Remarks -- Acknowledgments -- References -- 5 CLUSTERING: UNSUPERVISED LEARNING IN LARGE BIOLOGICAL DATA -- 5.1 Measures of Similarity -- 5.2 Clustering -- 5.3 Assessment of Cluster Quality -- 5.4 Conclusion -- References -- 6 CLASSIFICATION: SUPERVISED LEARNING WITH HIGH-DIMENSIONAL BIOLOGICAL DATA -- 6.1 Introduction -- 6.2 Classification and Prediction Methods -- 6.3 Feature Selection and Ranking -- 6.4 Cross-Validation -- 6.5 Enhancement of Class Prediction by Ensemble Voting Methods -- 6.6 Comparison of Classification Methods Using High-Dimensional Data -- 6.7 Software Examples for Classification Methods
References -- 7 MULTIDIMENSIONAL ANALYSIS AND VISUALIZATION ON LARGE BIOMEDICAL DATA -- 7.1 Introduction -- 7.2 Classical Multidimensional Visualization Techniques -- 7.3 Two-Dimensional Projections -- 7.4 Issues and Challenges -- 7.5 Systematic Exploration of Low-Dimensional Projections -- 7.6 One-Dimensional Histogram Ordering -- 7.7 Two-Dimensional Scatterplot Ordering -- 7.8 Conclusion -- References -- 8 STATISTICAL MODELS, INFERENCE, AND ALGORITHMS FOR LARGE BIOLOGICAL DATA ANALYSIS -- 8.1 Introduction -- 8.2 Statistical/Probabilistic Models -- 8.3 Estimation Methods -- 8.4 Numerical Algorithms -- 8.5 Examples -- 8.6 Conclusion -- References -- 9 EXPERIMENTAL DESIGNS ON HIGH-THROUGHPUT BIOLOGICAL EXPERIMENTS -- 9.1 Randomization -- 9.2 Replication -- 9.3 Pooling -- 9.4 Blocking -- 9.5 Design for Classifications -- 9.6 Design for Time Course Experiments -- 9.7 Design for eQTL Studies -- References -- 10 STATISTICAL RESAMPLING TECHNIQUES FOR LARGE BIOLOGICAL DATA ANALYSIS -- 10.1 Introduction -- 10.2 Resampling Methods for Prediction Error Assessment and Model Selection -- 10.3 Feature Selection -- 10.4 Resampling-Based Classification Algorithms -- 10.5 Practical Example: Lymphoma -- 10.6 Resampling Methods -- 10.7 Bootstrap Methods -- 10.8 Sample Size Issues -- 10.9 Loss Functions -- 10.10 Bootstrap Resampling for Quantifying Uncertainty -- 10.11 Markov Chain Monte Carlo Methods -- 10.12 Conclusions -- References -- 11 STATISTICAL NETWORK ANALYSIS FOR BIOLOGICAL SYSTEMS AND PATHWAYS -- 11.1 Introduction -- 11.2 Boolean Network Modeling -- 11.3 Bayesian Belief Network -- 11.4 Modeling of Metabolic Networks -- References -- 12 TRENDS AND STATISTICAL CHALLENGES IN GENOMEWIDE ASSOCIATION STUDIES -- 12.1 Introduction -- 12.2 Alleles, Linkage Disequilibrium, and Haplotype -- 12.3 International HapMap Project -- 12.4 Genotyping Platforms
12.5 Overview of Current GWAS Results -- 12.6 Statistical Issues in GWAS -- 12.7 Haplotype Analysis -- 12.8 Homozygosity and Admixture Mapping -- 12.9 Gene × Gene and Gene × Environment Interactions -- 12.10 Gene and Pathway-Based Analysis -- 12.11 Disease Risk Estimates -- 12.12 Meta-Analysis -- 12.13 Rare Variants and Sequence-Based Analysis -- 12.14 Conclusions -- Acknowledgments -- References -- 13 R AND BIOCONDUCTOR PACKAGES IN BIOINFORMATICS: TOWARDS SYSTEMS BIOLOGY -- 13.1 Introduction -- 13.2 Brief overview of the Bioconductor Project -- 13.3 Experimental Data -- 13.4 Annotation -- 13.5 Models of Biological Systems -- 13.6 Conclusion -- 13.7 Acknowledgments -- References -- INDEX
A practical introduction to the underlying statistical concepts and techniques for successful use of bioinformatics tools Effective use of the tools and methods of bioinformatics requires a careful understanding of not only the relevant biology and computational problems, but also critical statistical principles. Statistical Bioinformatics provides an essential understanding of the novel statistical concepts necessary for the analysis of genomic and proteomic data using various bioinformatics and computational techniques. Dr. Jae Lee and the authors present both basic and advanced topics, focusing on those that are relevant to the efficient and rigorous analysis of large data sets in biology. The book starts with an introduction to probability and statistics for genome-wide data, and moves into topics such as clustering, classification, multidimensional visualization, experimental design, statistical resampling, and statistical network analysis. Chapters begin with a description of a statistical concept and practical examples from biomedical research, followed by more detailed presentation, discussion of limitations, and problems. Clearly explains the use of bioinformatics tools in life sciences research without requiring an advanced background in math/statistics Enables biomedical and life sciences researchers to successfully evaluate the validity of their results and make inferences Enables statistical and quantitative researchers to rapidly learn novel statistical concepts and techniques appropriate for large biological data analysis Carefully revisits frequently used statistical approaches and highlights their limitations in large biological data analysis Offers programming examples and datasets Includes chapter problem sets, a glossary, a list of statistical notations, and appendices with references to background
mathematical and technical material Features supplementary materials, including datasets, links, and a statistical package available online Statistical Bioinformatics is an ideal textbook for students in medicine, life sciences, and bioengineering, aimed at researchers who utilize computational tools for the analysis of genomic, proteomic, and many other emerging high-throughput molecular data. It may also serve as a rapid introduction to the bioinformatics science for statistical and computational students and audiences who have not experienced such analysis tasks before
Description based on publisher supplied metadata and other sources
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
鏈接 Print version: Lee, Jae K. Statistical Bioinformatics : For Biomedical and Life Science Researchers Hoboken : John Wiley & Sons, Incorporated,c2014 9780471692720
主題 Bioinformatics -- Statistical methods.;Biology -- Data processing
Electronic books
Record:   Prev Next